Compressed Sensing for Engineers

Angshul Majumdar

ISBN 10: 1032338717 / ISBN 13: 9781032338712

Editore: CRC Press Jun 2022, 2022

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This item is printed on demand - it takes 3-4 days longer - Neuware -Compressed Sensing (CS) in theory deals with the problem of recovering a sparse signal from an under-determined system of linear equations. The topic is of immense practical significance since all naturally occurring signals can be sparsely represented in some domain. In recent years, CS has helped reduce scan time in Magnetic Resonance Imaging (making scans more feasible for pediatric and geriatric subjects) andhas also helped reducethe health hazard in X-Ray Computed CT. This book is a valuable resourcesuitable for an engineering student in signal processing and requires a basic understanding of signal processing and linear algebra.Covers fundamental concepts of compressed sensingMakes subject matter accessible for engineers of various levelsFocuses on algorithms including group-sparsity and row-sparsity, as well as applications to computational imaging, medical imaging, biomedical signal processing, and machine learningIncludes MATLAB examples for further development 294 pp. Englisch.

Codice articolo 9781032338712

Dati bibliografici

• Titolo: Compressed Sensing for Engineers
• Editore: CRC Press Jun 2022
• Data di pubblicazione: 2022
• Lingua: Inglese
• ISBN 10: 1032338717
• ISBN 13: 9781032338712
• Rilegatura: Taschenbuch
• Condizione: Neu
• Dimensioni: 234 L x 156 A x 16 P (millimetri) 234 millimetri di larghezza per 156 millimetri di altezza per 16 millimetri di profondità
• Peso: 450 grammi

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Riassunto

Compressed Sensing (CS) in theory deals with the problem of recovering a sparse signal from an under-determined system of linear equations. The topic is of immense practical significance since all naturally occurring signals can be sparsely represented in some domain. In recent years, CS has helped reduce scan time in Magnetic Resonance Imaging (making scans more feasible for pediatric and geriatric subjects) and has also helped reduce the health hazard in X-Ray Computed CT. This book is a valuable resource suitable for an engineering student in signal processing and requires a basic understanding of signal processing and linear algebra.

• Covers fundamental concepts of compressed sensing
• Makes subject matter accessible for engineers of various levels
• Focuses on algorithms including group-sparsity and row-sparsity, as well as applications to computational imaging, medical imaging, biomedical signal processing, and machine learning
• Includes MATLAB examples for further development

Informazioni sull?autore

Angshul Majumdar is currently an assistant professor in Electronics and Communications Engineering at the Indraprastha Institute of Information Technology, Delhi (IIIT-D). He completed his PhD in 2012 from the University of British Columbia, Canada. His main contribution is in reducing acquisition time for Magnetic Resonance Imaging acquisition. He has around 25 papers on this topic published in top-tier journals and conferences. Angshul also works in other areas of biomedical imaging and signal processing. Previously, he was interested in the problem of classification and has published several papers on robust classification techniques with applications in face recognition, fingerprint recognition and optical character recognition. In all, Angshul has published over 50 papers in top-tier journals and conferences in the last 5 years. Before Angshul started in the academia, he worked in business consulting at the Pricewaterhouse Coopers.